





'''simple docstring'''


import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


a__			:					str       						=						{
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

a__			:					Optional[Any]       						=						logging.WARNING
def       __snake_case		( )							->     List[str]:

       """simple docstring"""


       UpperCAmelCase  =						os.getenv('''DATASETS_VERBOSITY'''			,	SCREAMING_SNAKE_CASE_ )
       if env_level_str:
              if env_level_str in log_levels:
                     return log_levels[env_level_str]
              else:
                     logging.getLogger().warning(
                         f"Unknown option DATASETS_VERBOSITY={env_level_str}, "
                         f"has to be one of: { ', '.join(log_levels.keys() ) }" )
       return _default_log_level
def       __snake_case		( )							->     str:

       """simple docstring"""


       return __name__.split('''.''' )[0]
def       __snake_case		( )							->     logging.Logger:

       """simple docstring"""


       return logging.getLogger(_get_library_name() )
def       __snake_case		( )							->     None:

       """simple docstring"""


       UpperCAmelCase  =						_get_library_root_logger()
       library_root_logger.setLevel(_get_default_logging_level() )
def       __snake_case		( )							->     None:

       """simple docstring"""


       UpperCAmelCase  =						_get_library_root_logger()
       library_root_logger.setLevel(logging.NOTSET )
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[str] = None )							->     logging.Logger:

       """simple docstring"""


       if name is None:
              UpperCAmelCase  =						_get_library_name()
       return logging.getLogger(SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     int:

       """simple docstring"""


       return _get_library_root_logger().getEffectiveLevel()
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			int )							->     None:

       """simple docstring"""


       _get_library_root_logger().setLevel(SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     List[Any]:

       """simple docstring"""


       return set_verbosity(SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     Optional[int]:

       """simple docstring"""


       return set_verbosity(SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     List[Any]:

       """simple docstring"""


       return set_verbosity(SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     Optional[Any]:

       """simple docstring"""


       return set_verbosity(SCREAMING_SNAKE_CASE_ )
def       __snake_case		( )							->     None:

       """simple docstring"""


       UpperCAmelCase  =						False
def       __snake_case		( )							->     None:

       """simple docstring"""


       UpperCAmelCase  =						True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				List[str]       ,     *a__					:				str       ,     **a__					:				Optional[int]						):  # pylint: disable=unused-argument
              UpperCAmelCase  =						args[0] if args else None

       def __iter__( self					:				List[str]						):
              return iter(self._iterator						)

       def __getattr__( self					:				Optional[int]       ,     a__					:				str						):

              def empty_fn(*a__					:				str       ,     **a__					:				str						):  # pylint: disable=unused-argument
                     return

              return empty_fn

       def __enter__( self					:				int						):
              return self



       def __exit__( self					:				List[Any]       ,     a__					:				Dict       ,     a__					:				str       ,     a__					:				List[Any]						):
              return


a__			:					Optional[Any]       						=						True






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __call__( self					:				Any       ,     *a__					:				int       ,     a__					:				Any=False       ,     **a__					:				Union[str, Any]						):
              if _tqdm_active and not disable:
                     return tqdm_lib.tqdm(*a__       ,     **a__						)
              else:
                     return EmptyTqdm(*a__       ,     **a__						)

       def 			__snake_case      ( self					:				str       ,     *a__					:				Tuple       ,     **a__					:				Union[str, Any]						):
              UpperCAmelCase  =						None
              if _tqdm_active:
                     return tqdm_lib.tqdm.set_lock(*a__       ,     **a__						)



       def 			__snake_case      ( self					:				Tuple						):
              if _tqdm_active:
                     return tqdm_lib.tqdm.get_lock()


a__			:					Union[str, Any]       						=						_tqdm_cls()
def       __snake_case		( )							->     bool:

       """simple docstring"""


       global _tqdm_active
       return bool(_tqdm_active )
def       __snake_case		( )							->     List[Any]:

       """simple docstring"""


       global _tqdm_active
       UpperCAmelCase  =						True

def       __snake_case		( )							->     Union[str, Any]:

       """simple docstring"""


       global _tqdm_active
       UpperCAmelCase  =						False



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)